Directional sinogram interpolation for motion weighted 4D cone-beam CT reconstruction.

The image quality of respiratory sorted four-dimensional (4D) cone-beam (CB) computed tomography (CT) is often limited by streak artifacts due to insufficient projections. A motion weighted reconstruction (MWR) method is proposed to decrease streak artifacts and improve image quality. Firstly, respiratory correlated CBCT projections were interpolated by directional sinogram interpolation (DSI) to generate additional CB projections for each phase and subsequently reconstructed. Secondly, local motion was estimated by deformable image registration of the interpolated 4D CBCT. Thirdly, a regular 3D FDK CBCT was reconstructed from the non-interpolated projections. Finally, weights were assigned to each voxel, based on the local motion, and then were used to combine the 3D FDK CBCT and interpolated 4D CBCT to generate the final 4D image. MWR method was compared with regular 4D CBCT scans as well as McKinnon and Bates (MKB) based reconstructions. Comparisons were made in terms of (1) comparing the steepness of an extracted profile from the boundary of the region-of-interest (ROI), (2) contrast-to-noise ratio (CNR) inside certain ROIs, and (3) the root-mean-square-error (RMSE) between the planning CT and CBCT inside a homogeneous moving region. Comparisons were made for both a phantom and four patient scans. In a 4D phantom, RMSE were reduced by 24.7% and 38.7% for MKB and MWR respectively, compared to conventional 4D CBCT. Meanwhile, interpolation induced blur was minimal in static regions for MWR based reconstructions. In regions with considerable respiratory motion, image blur using MWR is less than the MKB and 3D Feldkamp (FDK) methods. In the lung cancer patients, average CNRs of MKB, DSI and MWR improved by a factor 1.7, 2.8 and 3.5 respectively relative to 4D FDK. MWR effectively reduces RMSE in 4D cone-beam CT and improves the image quality in both the static and respiratory moving regions compared to 4D FDK and MKB methods.

[1]  Guang-Hong Chen,et al.  Performance studies of four-dimensional cone beam computed tomography , 2011, Physics in medicine and biology.

[2]  J. Wong,et al.  Flat-panel cone-beam computed tomography for image-guided radiation therapy. , 2002, International journal of radiation oncology, biology, physics.

[3]  Til Aach,et al.  Analysis of Multiple Orientations , 2009, IEEE Transactions on Image Processing.

[4]  S. Rit,et al.  On-the-fly motion-compensated cone-beam CT using an a priori model of the respiratory motion. , 2009, Medical physics.

[5]  Josh Star-Lack,et al.  Fast 4D cone-beam reconstruction using the McKinnon-Bates algorithm with truncation correction and nonlinear filtering , 2011, Medical Imaging.

[6]  Nikos Paragios,et al.  DRAMMS: Deformable Registration via Attribute Matching and Mutual-Saliency Weighting , 2009, IPMI.

[7]  S. Leng,et al.  High temporal resolution and streak-free four-dimensional cone-beam computed tomography , 2008, Physics in medicine and biology.

[8]  David Sarrut,et al.  Deformable registration for image-guided radiation therapy. , 2006, Zeitschrift fur medizinische Physik.

[9]  T. M. Guerrero,et al.  Four-dimensional cone beam CT with adaptive gantry rotation and adaptive data sampling. , 2007, Medical physics.

[10]  M. V. van Herk,et al.  Respiratory correlated cone beam CT. , 2005, Medical physics.

[11]  Benoît Macq,et al.  Comparison of 12 deformable registration strategies in adaptive radiation therapy for the treatment of head and neck tumors. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[12]  Guang-Hong Chen,et al.  Streaking artifacts reduction in four-dimensional cone-beam computed tomography. , 2008, Medical physics.

[13]  Jan-Jakob Sonke,et al.  Comparative study of respiratory motion correction techniques in cone-beam computed tomography. , 2011, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  Til Aach,et al.  Analysis of Superimposed Oriented Patterns , 2006, IEEE Transactions on Image Processing.

[15]  Matthias Bertram,et al.  Directional View Interpolation for Compensation of Sparse Angular Sampling in Cone-Beam CT , 2009, IEEE Transactions on Medical Imaging.

[16]  Jan-Jakob Sonke,et al.  Directional sinogram interpolation for sparse angular acquisition in cone-beam computed tomography. , 2013, Journal of X-ray science and technology.

[17]  K. Rosenzweig,et al.  Correction of motion artifacts in cone-beam CT using a patient-specific respiratory motion model. , 2010, Medical physics.

[18]  Graeme C. Mc Kinnon,et al.  Towards Imaging the Beating Heart Usefully with a Conventional CT Scanner , 1981, IEEE Transactions on Biomedical Engineering.

[19]  Jan-Jakob Sonke,et al.  Comparison of different strategies to use four-dimensional computed tomography in treatment planning for lung cancer patients. , 2008, International journal of radiation oncology, biology, physics.

[20]  Nicole M Wink,et al.  Respiratory correlated cone-beam computed tomography on an isocentric C-arm , 2005, Physics in medicine and biology.

[21]  R. Marks.  Introduction to Shannon Sampling and Interpolation Theory , 1990 .

[22]  Jan-Jakob Sonke,et al.  Directional Interpolation for Motion Weighted 4D Cone-Beam CT Reconstruction , 2012, MICCAI.

[23]  T. Pan,et al.  Autoadaptive phase-correlated (AAPC) reconstruction for 4D CBCT. , 2009, Medical physics.

[24]  Frank Bergner,et al.  An investigation of 4D cone-beam CT algorithms for slowly rotating scanners. , 2010, Medical physics.

[25]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[26]  Xuejun Gu,et al.  Simultaneous motion estimation and image reconstruction (SMEIR) for 4D cone-beam CT , 2013, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).